Deep Learning

The program will primarily use the Python programming language and assumes familiarity with linear algebra, probability theory, and programming in Python.|KNOWLEDGE & SKILLS GAINED

Deep Learning students will apply clustering techniques on real-world data, train Deep Neural Networks and Multi-Layer Perceptron with TensorFlow, build anomaly detection system by separating outliers, and use Deep Architectures for problem modeling.
The student will be able to construct data representations using deep neural nets, build models for sentiment analysis, train and build models for image classification, work with sequences using recurrent neural networks and use Deep Belief networks for classification.
The project work will build your technical skills by providing a methodological approach towards problem-solving using models in Deep Learning.
Read more

NEXT PROGRAM START DATES
Call for Online Schedule

Select Campus/Location

Toggle Dropdown

Online Campus

Mississauga Malton Campus

Mississauga Trades

Brampton Campus

Mississauga Dundas Campus

Scarborough Campus

Hamilton Concession Campus

Hamilton King Campus

All Campus

START DATEEND DATEDAYTIMINGCAMPUSSTATUSREGISTER

May 24, 2022

September 14, 2022

MON, TUE, WED, THU, FRI
9:30 AM – 2:30 PM
Mississauga – Malton Campus

Register Now

Register Now

May 24, 2022

September 14, 2022

MON, TUE, WED, THU, FRI
9:30 AM – 2:30 PM
Online Campus

Register Now

Register Now

November 21, 2022

March 20, 2023

MON, TUE, WED, THU, FRI
5:00 PM – 10:00 PM
Online Campus

Register Now

Register Now

November 21, 2022

March 20, 2023

MON, TUE, WED, THU, FRI
5:00 PM – 10:00 PM
Mississauga – Malton Campus

Register Now

Register Now

View All Schedules

Contact us directly for other start dates

Seats Available

Seats Available

Limited Seats

Class Full

$(document).ready(function () {
“use strict”;

BindScheduleTable();
});
//this section find your partial postback and rebind after postback end.
var prm = Sys.WebForms.PageRequestManager.getInstance();
prm.add_endRequest(function () {

BindScheduleTable();
});

function BindScheduleTable() {
$(‘.jid-schedule-table-1’).footable();
var rowcount = $(“.jid-scheduletablerow-1”).length;
//Showing view more less button for schedulerows greater than 5 / else disable
if (rowcount > 5) {
//initiate to hide table rows greater than 5, gt zero based index
$(‘.jid-scheduletablerow-1:gt(4)’).hide();
$(“#ScheduleTableViewAll-1”).click(function () {
if ($(“.jid-scheduletablerow-1:gt(4)”).is(“:visible”)) {
$(“.jid-scheduletablerow-1:gt(4)”).hide(100);
$(“#ScheduleTableViewAll-1”).html(“ View All Schedules”).removeClass().addClass(“btn btn-lg btn-block btn-orange”);
}
else {
$(“.jid-scheduletablerow-1:gt(4)”).show(800);
$(“#ScheduleTableViewAll-1”).html(“ View Less Schedules”).removeClass().addClass(“btn btn-lg btn-block btn-blue”);
}
});
}
else {
$(‘#ScheduleTableViewAll-1′).hide();
}
}

PRE-REQUISITES: Computer Knowledge

PROGRAM OUTLINE

Professional Skills -?Trains on interpersonal skills required for successful induction and working in professional world.
Project+ – ? This CompTIA training course helps you to prepare for CompTIA Project+ exam.
Introduction to Programming:? This instructor-led course is intended for students who want to learn how to write Python code that logically solves a given problem.
Database Fundamentals – ? This MTA Training course helps you prepare for Microsoft Technology Associate Exam 98-364, and build an understanding of these topics: Core Database Concepts, Creating Database Objects, Manipulating Data, Data Storage, and Administering a Database.
Statistical Inference – The course introduces the fundamental concepts for understanding statistical analysis and its use in testing hypothesis and exploratory data analysis.
Mathematics for AI and ML – The course mathematical background for Artificial Intelligence and Machine Learning and covers ?Linear Algebra, Analytical Geometry, Matrix Decomposition, Probability Theory, Vector Calculus, and Optimization.
Data Processing and Manipulation – This course will introduce the concepts of data processing, wrangling, manipulation, transformation, and operations on data using Pandas Library.
Data Visualization – This course will provide the necessary skills to interpret and present data clearly and visually appealing using Python libraries.
Neural Networks and Deep Learning – ? This course presents the basic fundamentals for building Neural Networks and Deep Learning training models and covers topics like Multi-Layer Feed Forward Networks, Restricted Boltzmann Machines, Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks, and Recursive Neural Networks.
Deep Learning in Practice – ? The course familiarizes with essential Deep Learning architectural implementations in various applications such as Computer Vision, Recommender Systems, Text Analysis and Sequencing, and Natural Language Processing using TensorFlow.
Capstone Project for Deep Learning – ? This capstone project provides a hands-on learning experience for students to work independently on a specialized topic from Deep Learning.

WHY THIS PROGRAM?

Earning a?Diploma in Deep Learning opens doors to a vast array of opportunities in IT industry. This diploma covers topics such as deep neural networks, deep belief network, image models, and sequence models. Students are therefore trained in emerging applications areas of Deep Learning such as Computer Vision, Object Identification and Recommender System. Top IT employers are looking for deep learning engineers who understand the complexity of data problems in industry and can collaborate towards problem solving using models of Deep Learning.

LEARNING STEPS TOWARDS OTHER SIMILAR PROGRAMS

ASSOCIATED NATIONAL OCCUPATION CLASSIFICATION (NOC) CODES

After earning a Diploma in Deep Learning, graduates are eligible for positions falling under the following NOC Codes

2173-Software engineers and designer?s research, design, evaluate, integrate and maintain software applications, technical environments, operating systems, embedded software, information warehouses and telecommunications software. They are employed in information technology consulting firms, information technology research and development firms, and information technology units throughout the private and public sectors, or they may be self-employed.
2171- Information systems analysts and consultants analyze and test systems requirements, develop and implement information systems development plans, policies and procedures, and provide advice on a wide range of information systems issues. They are employed in information technology consulting firms and in information technology units throughout the public and private sectors, or they may be self-employed.
2174- Computer programmers write, modify, integrate and test computer code for software applications, data processing applications, operating systems-level software and communications software. Interactive media developers write, modify, integrate and test computer code for Internet and mobile applications, computer-based training software, computer games, film, video and other interactive media. They are employed in computer software development firms, information technology consulting firms, and in information technology units throughout the private and public sectors.

JOB FUNCTIONS

Software Engineers and Information Technology Associates?perform some or all of the following duties:
Software engineers and designers research, design, evaluate, integrate and maintain software applications, technical environments, operating systems, embedded software, information warehouses and telecommunications software. They are employed in information technology consulting firms, information technology research and development firms, and information technology units throughout the private and public sectors, or they may be self-employed.

Collect and document users’ requirements and develop logical and physical specifications
Research, evaluate and synthesize technical information to design, develop and test AI and ML based systems
Build, test, and deploy AI models, as well as maintain the underlying AI infrastructure.
Develop data, process and network models to optimize architecture and to evaluate the performance and reliability of designs
Can navigate between traditional software development and machine learning implementations.
Plan, design and co-ordinate the development, installation, integration and operation of AI based systems.
Assess, test, troubleshoot, document, upgrade and develop maintenance procedures for operating systems, communications environments and applications software using AI development and production infrastructure.
Apply mathematical techniques to the solution of problems in scientific fields such as physical science, engineering, computer science or other fields such as operations research, business or management.
Collect, consolidate, cross-tabulate and format data from various sources to prepare draft reports for review by supervisors or researchers.
Process data using statistical software to conduct basic analyses of trends in support of research activities
Describe, prepare, format and scale the data infrastructure for analysis and implementation.
Train machine-learning based software for domain-specific tasks (Image recognition, object detection, bioinformatics, autonomous vehicles, etc)

May lead and co-ordinate teams of information systems professionals in the development of software and integrated information systems, process control software and other embedded software control systems.

JOB REQUIREMENTS

A bachelor’s degree or college diploma in Deep Learning.
Job openings in the field of Information Technology usually seek a person with a diploma in Deep Learning or have studied related fields of study. Majority of the IT companies are looking with software engineers who can build deep learning models and optimize it, design and develop state-of-the-art deep learning models for object detection, classification, tracking, and segmentation.
Job openings in the field of Deep Learning usually seek a person who can work with Python ML Libraries such as numpy, pandas, Keras and Tensorflow in expertise in one or more areas of Computer Vision, Deep Learning, 3D-Reconstruct, Video Stitching and Image Signal Processing. The person can contribute towards the development and deployment of Deep Learning algorithms to build models in scale.

EMPLOYMENT AREAS & PROSPECTS

Completing the Diploma in Deep Learning exposes students to various fields for hire, predominantly for Deep Learning Engineer, Deep Reinforcement Learning Expert, software engineers, information systems, information analysts, research analyst, data management, and data analysis in various IT industries. The technology sector in Canada is growing at an expansive rate which is allowing for new opportunities to arise in this field.
The job market for graduates with understanding in Artificial Intelligence, Machine Learning and Deep Learning in IT industry is projected to grow 56% in the next few years and create new jobs as the number of companies expanding their IT departments, are growing.